In many businesses, data is collected as part of day-to-day operations. Trends and facts can be extracted from this data to give many meaningful insights into a business's performance in relation to goods or services it provides, or, more generally, for the purposes of operational research.
One known paradigm for the analysis of data is the on-line analytical processing (OLAP) model (see for example S. Chaudhari and U. Dayal, An Overview of Data Warehousing and OLAP Technology, ACM SIGMOD Record, June 1997, pp. 65-74). OLAP software applications allow the collection, storage, manipulation and reproduction of multi-dimensional data. By analysing data along the various dimensions and looking at the measures of interest, one can discover interesting correlations. The process of discovering items of interest is interactive in nature, requiring user input and manual analysis. The manual aspect of the analysis suffers from the problem of being time consuming and tedious, in that a number of queries need to be conducted to arrive at the information of interest, and furthermore, insights may be overlooked due to a particular query being skipped by the user.
OLAP queries aggregate measures in data in various ways at different levels of the dimensional hierarchy. A Graphical User Interface approach has been used to represent the hierarchical data in the form of data cubes. The user is able to enter cubes and navigate in conducting an analysis to locate interesting parts within the data (see for example Sarawagi et al., Discovery-driven Exploration of OLAP data cubes, in Proc. of the 6th International Conference on Extending Database Technology (EDBT), Valencia, Spain, 1998).
Earlier approaches have focused on providing tools and methods for a user to identify significant information. The user still has to play an active role and navigate the data cube using the tools.